Lung Cancer

Detailed Survival analyis of the Survival lung data.

Libraries

library(survival)
library(FRESA.CAD)

## Loading required package: Rcpp

## Loading required package: stringr

## Loading required package: miscTools

## Loading required package: Hmisc

## 
## Attaching package: 'Hmisc'

## The following objects are masked from 'package:base':
## 
##     format.pval, units

## Loading required package: pROC

## Type 'citation("pROC")' for a citation.

## 
## Attaching package: 'pROC'

## The following objects are masked from 'package:stats':
## 
##     cov, smooth, var

op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('keep.trailing.zeros',TRUE)

Libraries

data(lung)

## Warning in data(lung): data set 'lung' not found

lung$inst <- NULL
lung$status <- lung$status - 1
lung <- lung[complete.cases(lung),]

pander::pander(table(lung$status))

0	1
47	121

pander::pander(summary(lung$time))

Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
5	175	268	310	416	1022

Exploring Raw Features with RRPlot

convar <- colnames(lung)[lapply(apply(lung,2,unique),length) > 10]
convar <- convar[convar != "time"]
topvar <- univariate_BinEnsemble(lung[,c("status",convar)],"status")
pander::pander(topvar)

age	wt.loss
0.106	0.106

topv <- min(5,length(topvar))
topFive <- names(topvar)[1:topv]
RRanalysis <- list();
idx <- 1
for (topf in topFive)
{
  RRanalysis[[idx]] <- RRPlot(cbind(lung$status,lung[,topf]),
                              atRate=c(0.90),
                  timetoEvent=lung$time,
                  title=topf,
#                  plotRR=FALSE
                  )
  idx <- idx + 1
}

names(RRanalysis) <- topFive

Reporting the Metrics

ROCAUC <- NULL
CstatCI <- NULL
LogRangp <- NULL
Sensitivity <- NULL
Specificity <- NULL

for (topf in topFive)
{
  CstatCI <- rbind(CstatCI,RRanalysis[[topf]]$c.index$cstatCI)
  LogRangp <- rbind(LogRangp,RRanalysis[[topf]]$surdif$pvalue)
  Sensitivity <- rbind(Sensitivity,RRanalysis[[topf]]$ROCAnalysis$sensitivity)
  Specificity <- rbind(Specificity,RRanalysis[[topf]]$ROCAnalysis$specificity)
  ROCAUC <- rbind(ROCAUC,RRanalysis[[topf]]$ROCAnalysis$aucs)
}
rownames(CstatCI) <- topFive
rownames(LogRangp) <- topFive
rownames(Sensitivity) <- topFive
rownames(Specificity) <- topFive
rownames(ROCAUC) <- topFive

pander::pander(ROCAUC)

	est	lower	upper
age	0.590	0.493	0.688
wt.loss	0.553	0.455	0.651

pander::pander(CstatCI)

	mean.C Index	median	lower	upper
age	0.558	0.559	0.505	0.612
wt.loss	0.511	0.511	0.453	0.572

pander::pander(LogRangp)

age	0.919
wt.loss	0.358

pander::pander(Sensitivity)

	est	lower	upper
age	0.1157	0.0647	0.187
wt.loss	0.0496	0.0184	0.105

pander::pander(Specificity)

	est	lower	upper
age	0.872	0.743	0.952
wt.loss	0.894	0.769	0.965

meanMatrix <- cbind(ROCAUC[,1],CstatCI[,1],Sensitivity[,1],Specificity[,1])
colnames(meanMatrix) <- c("ROCAUC","C-Stat","Sen","Spe")
pander::pander(meanMatrix)

	ROCAUC	C-Stat	Sen	Spe
age	0.590	0.558	0.1157	0.872
wt.loss	0.553	0.511	0.0496	0.894

Modeling

ml <- BSWiMS.model(Surv(time,status)~1,data=lung,NumberofRepeats = 10)

[+++++++++++++++++++++++++++++]..

sm <- summary(ml)
pander::pander(sm$coefficients)

Table continues below

	Estimate	lower	HR	upper	u.Accuracy	r.Accuracy
ph.ecog	4.32e-01	1.194	1.541	1.988	0.679	0.649
sex	-4.59e-01	0.456	0.632	0.876	0.649	0.679
pat.karno	-1.77e-03	0.997	0.998	1.000	0.506	0.720
ph.karno	-4.64e-07	1.000	1.000	1.000	0.577	0.720
age	4.57e-08	1.000	1.000	1.000	0.565	0.720

Table continues below

	full.Accuracy	u.AUC	r.AUC	full.AUC	IDI	NRI
ph.ecog	0.601	0.601	0.620	0.600	0.0449	0.405
sex	0.601	0.620	0.601	0.600	0.0285	0.478
pat.karno	0.506	0.585	0.500	0.585	0.0292	0.342
ph.karno	0.577	0.570	0.500	0.570	0.0143	0.280
age	0.565	0.549	0.500	0.549	0.0162	0.195

	z.IDI	z.NRI	Delta.AUC	Frequency
ph.ecog	3.33	2.48	-0.02005	1.0
sex	2.76	2.85	-0.00167	1.0
pat.karno	2.44	2.24	0.08546	1.0
ph.karno	2.22	1.64	0.06998	0.8
age	1.97	1.14	0.04871	0.1

Cox Model Performance

Here we evaluate the model using the RRPlot() function.

The evaluation of the raw Cox model with RRPlot()

Here we will use the predicted event probability assuming a baseline hazard for events withing 5 years

timeinterval <- 2*mean(subset(lung,status==1)$time)

h0 <- sum(lung$status & lung$time <= timeinterval)
h0 <- h0/sum((lung$time > timeinterval) | (lung$status==1))
pander::pander(t(c(h0=h0,timeinterval=timeinterval)),caption="Initial Parameters")

Initial Parameters

h0	timeinterval
0.85	578

index <- predict(ml,lung)

rdata <- cbind(lung$status,ppoisGzero(index,h0))

rrAnalysisTrain <- RRPlot(rdata,atRate=c(0.90),
                     timetoEvent=lung$time,
                     title="Raw Train: Lung Cancer",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

Expected time to event

toinclude <- rdata[,1]==1 
obstiemToEvent <- lung$time
tmin<-min(obstiemToEvent)
sum(toinclude)

[1] 121

timetoEvent <- meanTimeToEvent(rdata[,2],timeinterval)
tmax<-max(c(obstiemToEvent,timetoEvent))
lmfit <- lm(obstiemToEvent[toinclude]~0+timetoEvent[toinclude])
sm <- summary(lmfit)
pander::pander(sm)

	Estimate	Std. Error	t value	Pr(>|t|)
timetoEvent[toinclude]	0.808	0.0509	15.9	2.92e-31

Fitting linear model: obstiemToEvent[toinclude] ~ 0 + timetoEvent[toinclude]

Observations	Residual Std. Error	\(R^2\)	Adjusted \(R^2\)
121	200	0.677	0.675

plot(timetoEvent,obstiemToEvent,
     col=1+rdata[,1],
     xlab="Expected time",
     ylab="Observed time",
     main="Expected vs. Observed",
     xlim=c(tmin,tmax),
     ylim=c(tmin,tmax),
     log="xy")
lines(x=c(tmin,tmax),y=lmfit$coefficients*c(tmin,tmax),lty=1,col="blue")
txt <- bquote(paste(R^2 == .(round(sm$r.squared,3))))
text(tmin+0.005*(tmax-tmin),tmax,txt,cex=0.7)
text(tmin+0.015*(tmax-tmin),tmax,sprintf("Slope=%4.3f",sm$coefficients[1]),cex=0.7)
legend("bottomright",legend=c("No Event","Event","Linear fit"),
             pch=c(1,1,-1),
             col=c(1,2,"blue"),
             lty=c(-1,-1,1)
             )

MADerror2 <- mean(abs(timetoEvent[toinclude]-obstiemToEvent[toinclude]))
pander::pander(MADerror2)

163

Uncalibrated Performance Report

pander::pander(t(rrAnalysisTrain$keyPoints),caption="Threshold values")

Threshold values

	@:0.9	@MAX_BACC	@MAX_RR	@SPE100	p(0.5)
Thr	0.649	0.478	3.39e-01	3.39e-01	0.493
RR	1.214	1.742	6.85e+01	6.85e+01	1.270
RR_LCI	1.014	1.260	1.44e-01	1.44e-01	1.037
RR_UCI	1.454	2.408	3.26e+04	3.26e+04	1.555
SEN	0.314	0.826	1.00e+00	1.00e+00	0.612
SPE	0.830	0.511	1.91e-01	1.91e-01	0.596
BACC	0.572	0.669	5.96e-01	5.96e-01	0.604
NetBenefit	0.138	0.470	6.04e-01	6.04e-01	0.331

pander::pander(t(rrAnalysisTrain$OERatio$estimate),caption="O/E Ratio")

O/E Ratio

O/E	Low	Upper	p.value
1.57	1.3	1.87	3.7e-06

pander::pander(t(rrAnalysisTrain$OE95ci),caption="O/E Mean")

O/E Mean

mean	50%	2.5%	97.5%
1.23	1.23	1.19	1.27

pander::pander(t(rrAnalysisTrain$OAcum95ci),caption="O/Acum Mean")

O/Acum Mean

mean	50%	2.5%	97.5%
1.27	1.27	1.26	1.29

pander::pander(rrAnalysisTrain$c.index$cstatCI,caption="C. Index")

mean.C Index	median	lower	upper
0.651	0.651	0.589	0.711

pander::pander(t(rrAnalysisTrain$ROCAnalysis$aucs),caption="ROC AUC")

ROC AUC

est	lower	upper
0.691	0.598	0.784

pander::pander((rrAnalysisTrain$ROCAnalysis$sensitivity),caption="Sensitivity")

Sensitivity

est	lower	upper
0.314	0.233	0.405

pander::pander((rrAnalysisTrain$ROCAnalysis$specificity),caption="Specificity")

Specificity

est	lower	upper
0.83	0.692	0.924

pander::pander(t(rrAnalysisTrain$thr_atP),caption="Probability Thresholds")

Probability Thresholds

90%
0.648

pander::pander(rrAnalysisTrain$surdif,caption="Logrank test")

Logrank test Chisq = 7.945448 on 1 degrees of freedom, p = 0.004821

	N	Observed	Expected	(O-E)^2/E	(O-E)^2/V
class=0	122	83	95.6	1.65	7.95
class=1	46	38	25.4	6.22	7.95

Cox Calibration

op <- par(no.readonly = TRUE)


calprob <- CoxRiskCalibration(ml,lung,"status","time")

pander::pander(c(h0=calprob$h0,
                 Gain=calprob$hazardGain,
                 DeltaTime=calprob$timeInterval),
               caption="Cox Calibration Parameters")

h0	Gain	DeltaTime
1.55	1.83	863

The RRplot() of the calibrated model

h0 <- calprob$h0
timeinterval <- calprob$timeInterval;

rdata <- cbind(lung$status,calprob$prob)


rrAnalysisTrain <- RRPlot(rdata,atRate=c(0.90),
                     timetoEvent=lung$time,
                     title="Train Cal: Lung",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

Expected time to event

timetoEvent <- meanTimeToEvent(rdata[,2],timeinterval)
tmax<-max(c(obstiemToEvent,timetoEvent))
lmfit <- lm(obstiemToEvent[toinclude]~0+timetoEvent[toinclude])
sm <- summary(lmfit)
pander::pander(sm)

	Estimate	Std. Error	t value	Pr(>|t|)
timetoEvent[toinclude]	0.99	0.0623	15.9	2.92e-31

Fitting linear model: obstiemToEvent[toinclude] ~ 0 + timetoEvent[toinclude]

Observations	Residual Std. Error	\(R^2\)	Adjusted \(R^2\)
121	200	0.677	0.675

plot(timetoEvent,obstiemToEvent,
     col=1+rdata[,1],
     xlab="Expected time",
     ylab="Observed time",
     main="Expected vs. Observed",
     xlim=c(tmin,tmax),
     ylim=c(tmin,tmax),
     log="xy")
lines(x=c(tmin,tmax),y=lmfit$coefficients*c(tmin,tmax),lty=1,col="blue")
txt <- bquote(paste(R^2 == .(round(sm$r.squared,3))))
text(tmin+0.005*(tmax-tmin),tmax,txt,cex=0.7)
text(tmin+0.015*(tmax-tmin),tmax,sprintf("Slope=%4.3f",sm$coefficients[1]),cex=0.7)
legend("bottomright",legend=c("No Event","Event","Linear fit"),
             pch=c(1,1,-1),
             col=c(1,2,"blue"),
             lty=c(-1,-1,1)
             )

MADerror2 <-c(MADerror2,mean(abs(timetoEvent-obstiemToEvent)))
pander::pander(MADerror2)

163 and 165

Calibrated Train Performance

pander::pander(t(rrAnalysisTrain$keyPoints),caption="Threshold values")

Threshold values

	@:0.9	@MAX_BACC	@MAX_RR	@SPE100	p(0.5)
Thr	0.8523	0.695	5.31e-01	5.31e-01	5.31e-01
RR	1.2142	1.742	6.85e+01	6.85e+01	6.85e+01
RR_LCI	1.0143	1.260	1.44e-01	1.44e-01	1.44e-01
RR_UCI	1.4536	2.408	3.26e+04	3.26e+04	3.26e+04
SEN	0.3140	0.826	1.00e+00	1.00e+00	1.00e+00
SPE	0.8298	0.511	1.91e-01	1.91e-01	1.91e-01
BACC	0.5719	0.669	5.96e-01	5.96e-01	5.96e-01
NetBenefit	-0.0484	0.283	4.64e-01	4.64e-01	4.64e-01

pander::pander(t(rrAnalysisTrain$OERatio$estimate),caption="O/E Ratio")

O/E Ratio

O/E	Low	Upper	p.value
1.28	1.06	1.53	0.00868

pander::pander(t(rrAnalysisTrain$OE95ci),caption="O/E Mean")

O/E Mean

mean	50%	2.5%	97.5%
1.01	1	0.971	1.04

pander::pander(t(rrAnalysisTrain$OAcum95ci),caption="O/Acum Mean")

O/Acum Mean

mean	50%	2.5%	97.5%
1.02	1.02	1.01	1.02

pander::pander(rrAnalysisTrain$c.index$cstatCI,caption="C. Index")

mean.C Index	median	lower	upper
0.651	0.652	0.596	0.71

pander::pander(t(rrAnalysisTrain$ROCAnalysis$aucs),caption="ROC AUC")

ROC AUC

est	lower	upper
0.691	0.598	0.784

pander::pander((rrAnalysisTrain$ROCAnalysis$sensitivity),caption="Sensitivity")

Sensitivity

est	lower	upper
0.314	0.233	0.405

pander::pander((rrAnalysisTrain$ROCAnalysis$specificity),caption="Specificity")

Specificity

est	lower	upper
0.83	0.692	0.924

pander::pander(t(rrAnalysisTrain$thr_atP),caption="Probability Thresholds")

Probability Thresholds

90%
0.851

pander::pander(rrAnalysisTrain$surdif,caption="Logrank test")

Logrank test Chisq = 7.945448 on 1 degrees of freedom, p = 0.004821

	N	Observed	Expected	(O-E)^2/E	(O-E)^2/V
class=0	122	83	95.6	1.65	7.95
class=1	46	38	25.4	6.22	7.95

Cross-Validation

rcv <- randomCV(theData=lung,
                theOutcome = Surv(time,status)~1,
                fittingFunction=BSWiMS.model, 
                trainFraction = 0.95,
                repetitions=200,
                classSamplingType = "Pro"
         )

.[+++].[++].[+++].[++].[+++].[+++].[++].[+++].[+++].[+]10 Tested: 77 Avg. Selected: 3.5 Min Tests: 1 Max Tests: 3 Mean Tests: 1.298701 . MAD: 0.4783289

.[++].[+++].[++].[+++].[+++].[+++].[+++].[++++].[++].[+++]20 Tested: 114 Avg. Selected: 3.65 Min Tests: 1 Max Tests: 6 Mean Tests: 1.754386 . MAD: 0.4773353

.[+-].[++++].[++].[+++].[++].[++].[++].[++].[+++].[++++]30 Tested: 138 Avg. Selected: 3.6 Min Tests: 1 Max Tests: 7 Mean Tests: 2.173913 . MAD: 0.4785659

.[+++].[+++].[++-].[+++].[++].[++].[+++].[++].[+++].[++]40 Tested: 150 Avg. Selected: 3.575 Min Tests: 1 Max Tests: 8 Mean Tests: 2.666667 . MAD: 0.4800767

.[+++].[++].[+++-].[+++].[++++].[+++].[+++].[++++].[+++].[+++]50 Tested: 157 Avg. Selected: 3.66 Min Tests: 1 Max Tests: 9 Mean Tests: 3.184713 . MAD: 0.4793113

.[+-].[+++].[+++].[++++].[+++].[+++].[++++].[+++].[+++].[+++]60 Tested: 158 Avg. Selected: 3.716667 Min Tests: 1 Max Tests: 10 Mean Tests: 3.797468 . MAD: 0.4788951

.[++].[+++].[+++].[++++].[++].[++].[+++].[+++].[+++].[++]70 Tested: 160 Avg. Selected: 3.714286 Min Tests: 1 Max Tests: 13 Mean Tests: 4.375 . MAD: 0.4792667

.[+++].[+++].[++-].[++].[+++].[+++].[+++].[+++].[+].[++]80 Tested: 166 Avg. Selected: 3.6875 Min Tests: 1 Max Tests: 13 Mean Tests: 4.819277 . MAD: 0.475646

.[+++].[++].[++].[+].[+++].[+++].[+++].[+++].[+++].[+++]90 Tested: 168 Avg. Selected: 3.677778 Min Tests: 1 Max Tests: 14 Mean Tests: 5.357143 . MAD: 0.475111

.[+++].[++].[+++].[++++].[+++].[++++].[+++].[+++].[+++].[++++]100 Tested: 168 Avg. Selected: 3.71 Min Tests: 1 Max Tests: 16 Mean Tests: 5.952381 . MAD: 0.4752537

.[++++].[++++].[+++].[+++].[+++].[+].[++].[++].[+].[+++]110 Tested: 168 Avg. Selected: 3.7 Min Tests: 1 Max Tests: 16 Mean Tests: 6.547619 . MAD: 0.4750302

.[+++].[+++].[+++].[+++].[++].[+++].[+].[+++].[++].[++]120 Tested: 168 Avg. Selected: 3.683333 Min Tests: 1 Max Tests: 16 Mean Tests: 7.142857 . MAD: 0.4748065

.[+++].[++].[+++].[+++].[+++].[+++].[+++].[+++].[++].[++]130 Tested: 168 Avg. Selected: 3.684615 Min Tests: 2 Max Tests: 17 Mean Tests: 7.738095 . MAD: 0.4749498

.[+++].[+++].[+++].[+].[++].[+++].[+++].[+++].[+++].[+++]140 Tested: 168 Avg. Selected: 3.685714 Min Tests: 2 Max Tests: 17 Mean Tests: 8.333333 . MAD: 0.4746038

.[+++].[+++].[+++].[+++].[+-].[+++].[+++].[++].[+++].[+++]150 Tested: 168 Avg. Selected: 3.686667 Min Tests: 2 Max Tests: 20 Mean Tests: 8.928571 . MAD: 0.4746182

.[++].[+++].[++].[+].[+++].[++].[++].[++].[+].[+++]160 Tested: 168 Avg. Selected: 3.65 Min Tests: 2 Max Tests: 22 Mean Tests: 9.52381 . MAD: 0.4744816

.[++].[+++].[+++].[+++].[+++].[+++].[+++].[+++].[+++].[++]170 Tested: 168 Avg. Selected: 3.658824 Min Tests: 3 Max Tests: 23 Mean Tests: 10.11905 . MAD: 0.4743399

.[+++].[++].[+++].[+++].[+].[++].[++].[+++].[+++].[++]180 Tested: 168 Avg. Selected: 3.644444 Min Tests: 3 Max Tests: 23 Mean Tests: 10.71429 . MAD: 0.4743689

.[++].[+++].[++].[++].[++].[+++].[+++].[+++].[+++].[+++]190 Tested: 168 Avg. Selected: 3.642105 Min Tests: 3 Max Tests: 23 Mean Tests: 11.30952 . MAD: 0.4744639

.[+++].[+++].[+++].[+++].[++].[++].[+++].[+++].[++].[+++]200 Tested: 168 Avg. Selected: 3.645 Min Tests: 3 Max Tests: 26 Mean Tests: 11.90476 . MAD: 0.4742153

stp <- rcv$survTestPredictions
stp <- stp[!is.na(stp[,4]),]

bbx <- boxplot(unlist(stp[,1])~rownames(stp),plot=FALSE)
times <- bbx$stats[3,]
status <- boxplot(unlist(stp[,2])~rownames(stp),plot=FALSE)$stats[3,]
prob <- ppoisGzero(boxplot(unlist(stp[,4])~rownames(stp),plot=FALSE)$stats[3,],h0)

rdatacv <- cbind(status,prob)
rownames(rdatacv) <- bbx$names
names(times) <- bbx$names

rrAnalysisTest <- RRPlot(rdatacv,atRate=c(0.90),
                     timetoEvent=times,
                     title="Test: Lung Cancer",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

Cross-Validation Test Performance

pander::pander(t(rrAnalysisTest$keyPoints),caption="Threshold values")

Threshold values

	@:0.9	@MAX_BACC	@MAX_RR	@SPE100	p(0.5)
Thr	0.8629	0.680	0.587	0.505	0.505
RR	1.1859	2.738	3.510	1.000	1.000
RR_LCI	0.9720	1.385	1.473	0.000	0.000
RR_UCI	1.4469	5.413	8.365	0.000	0.000
SEN	0.1983	0.950	0.967	1.000	1.000
SPE	0.8936	0.319	0.298	0.000	0.000
BACC	0.5460	0.635	0.632	0.500	0.500
NetBenefit	-0.0443	0.279	0.418	0.435	0.435

pander::pander(t(rrAnalysisTest$OERatio$estimate),caption="O/E Ratio")

O/E Ratio

O/E	Low	Upper	p.value
1.25	1.04	1.5	0.0167

pander::pander(t(rrAnalysisTest$OE95ci),caption="O/E Mean")

O/E Mean

mean	50%	2.5%	97.5%
0.991	0.991	0.959	1.02

pander::pander(t(rrAnalysisTest$OAcum95ci),caption="O/Acum Mean")

O/Acum Mean

mean	50%	2.5%	97.5%
0.98	0.98	0.972	0.989

pander::pander(rrAnalysisTest$c.index$cstatCI,caption="C. Index")

mean.C Index	median	lower	upper
0.594	0.594	0.53	0.653

pander::pander(t(rrAnalysisTest$ROCAnalysis$aucs),caption="ROC AUC")

ROC AUC

est	lower	upper
0.611	0.512	0.711

pander::pander((rrAnalysisTest$ROCAnalysis$sensitivity),caption="Sensitivity")

Sensitivity

est	lower	upper
0.198	0.131	0.281

pander::pander((rrAnalysisTest$ROCAnalysis$specificity),caption="Specificity")

Specificity

est	lower	upper
0.894	0.769	0.965

pander::pander(t(rrAnalysisTest$thr_atP),caption="Probability Thresholds")

Probability Thresholds

90%
0.863

pander::pander(rrAnalysisTest$surdif,caption="Logrank test")

Logrank test Chisq = 2.422523 on 1 degrees of freedom, p = 0.119602

	N	Observed	Expected	(O-E)^2/E	(O-E)^2/V
class=0	139	97	103	0.355	2.42
class=1	29	24	18	2.035	2.42

Calibrating the test results

rdatacv <- cbind(status,prob,times)
calprob <- CalibrationProbPoissonRisk(rdatacv)

pander::pander(c(h0=calprob$h0,
                 Gain=calprob$hazardGain,
                 DeltaTime=calprob$timeInterval),
               caption="Cox Calibration Parameters")

h0	Gain	DeltaTime
0.85	1	874

timeinterval <- calprob$timeInterval;

rdata <- cbind(status,calprob$prob)


rrAnalysisTest <- RRPlot(rdata,atRate=c(0.90),
                     timetoEvent=times,
                     title="Calibrated Test: Lung",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

Calibrated Test Performance

pander::pander(t(rrAnalysisTest$keyPoints),caption="Threshold values")

Threshold values

	@:0.9	@MAX_BACC	@MAX_RR	@SPE100	p(0.5)
Thr	0.8629	0.680	0.587	0.505	0.505
RR	1.1859	2.738	3.510	1.000	1.000
RR_LCI	0.9720	1.385	1.473	0.000	0.000
RR_UCI	1.4469	5.413	8.365	0.000	0.000
SEN	0.1983	0.950	0.967	1.000	1.000
SPE	0.8936	0.319	0.298	0.000	0.000
BACC	0.5460	0.635	0.632	0.500	0.500
NetBenefit	-0.0443	0.279	0.418	0.435	0.435

pander::pander(t(rrAnalysisTest$OERatio$estimate),caption="O/E Ratio")

O/E Ratio

O/E	Low	Upper	p.value
1.27	1.05	1.52	0.0104

pander::pander(t(rrAnalysisTest$OE95ci),caption="O/E Mean")

O/E Mean

mean	50%	2.5%	97.5%
1	1	0.971	1.04

pander::pander(t(rrAnalysisTest$OAcum95ci),caption="O/Acum Mean")

O/Acum Mean

mean	50%	2.5%	97.5%
0.981	0.981	0.973	0.99

pander::pander(rrAnalysisTest$c.index$cstatCI,caption="C. Index")

mean.C Index	median	lower	upper
0.594	0.593	0.53	0.659

pander::pander(t(rrAnalysisTest$ROCAnalysis$aucs),caption="ROC AUC")

ROC AUC

est	lower	upper
0.611	0.512	0.711

pander::pander((rrAnalysisTest$ROCAnalysis$sensitivity),caption="Sensitivity")

Sensitivity

est	lower	upper
0.198	0.131	0.281

pander::pander((rrAnalysisTest$ROCAnalysis$specificity),caption="Specificity")

Specificity

est	lower	upper
0.894	0.769	0.965

pander::pander(t(rrAnalysisTest$thr_atP),caption="Probability Thresholds")

Probability Thresholds

90%
0.863

pander::pander(rrAnalysisTest$surdif,caption="Logrank test")

Logrank test Chisq = 2.422523 on 1 degrees of freedom, p = 0.119602

	N	Observed	Expected	(O-E)^2/E	(O-E)^2/V
class=0	139	97	103	0.355	2.42
class=1	29	24	18	2.035	2.42